Tag Archives: long-term problems

How Can We Best Advance Ecological Research

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Everyone who supports scientific research wishes to see progress in understanding of the problems being studied. Ecologists face four critical difficulties in achieving these goals of progress.

  •  There are far too many ecological problems to be sorted out given the number of ecologists in the scientific world. There is a partial solution to this issue by dividing the ecological research into two broad categories: large scale critical problems and small-scale local problems. Large-scale problems are those that apply to many communities and ecosystems, from the local to the global. Large scale problems require first an agreement of what these problems are and second how they should be approached – both difficult to achieve in the current research environment. So there is too little agreement even on this simple issue. Large-scale problems require a coordinated team effort and to date in ecology large-scale problems have been poorly addressed. Small scale problems can be studied with less person power and are much more common. In an ideal world, governments would fund large-scale research programs that require effort that stretches over many years and require considerable reliable funding, and smaller grants could support the many specific postgraduate studies with a time limit of 3-4 years. It would be optimal for these two groups to meld together but this is difficult to achieve. The whole system we have currently fights against this cooperation, partly because in the world of science most of the recognition and rewards go to individual scientists and not to a research team.
  •  A second problem is that there are two paradigms of ecological science that are only partly overlapping. The first paradigm in its strong form states that ecological science will advance most rapidly by means of descriptive studies of changes in communities and ecosystems. Gaiser et al. (2020) provide good examples of this approach. Long-Term-Ecological-Research (LTER) approaches are typically large-scale and rely too much on the assumptions of correlation = causation science, but this paradigm suggests that long-term data will lead us to the long-term understanding of communities and ecosystems. The second approach can be described as the mechanistic paradigm because it attempts to explain long- and short-term population and community changes from whatever cause using experimental methods (Krebs 2002). Hone and Krebs (2023) detail the evidence levels needed for strong inference in both short-term and long-term issues. Lindenmayer (2018) provides an excellent synopsis of the difficulties of long-term research and a synopsis of how we might act to answer ecological questions about future issues (Lindenmayer et al. 2015, Lindenmayer 2018).
  • The third difficulty is that the individual scientist as the unit of research makes it unlikely in the present organization of science funding that these two paradigms can be easily brought together. Scientists by and large wish to be independent, a desirable trait, but we can and should work in a team effort, an interlocking independent research program that has specified objectives that all are organized into a partnership. If you want examples of this approach, you have only to look at the successes and failures of many long-term ecological research (LTER) programs around the world. One example of a successful LTER research program in Austria is reviewed in Gingrich et al. (2016). Additional evaluations of current LTER projects can be found in Vanderbilt and Gaiser (2017) and Rastetter et al. (2021). There has been a movement to integrate social and ecological frameworks to LTER research. A good example of this social-ecological approach is disturbance ecology described for the USA in Gaiser et al. (2020). But ecological approaches of the type described in Vanderbilt and Gaiser (2017) and Gaiser et al. (2020) appear to reduce ecological research to an endless study of descriptive changes in ecosystems with little theory. The hope is to advance ecological science by observations of changes in ecosystems as affected by human activity and climate change. The objective of these research programs is to provide solutions for future environmental problems from long term data sets. However, describing the past does not inherently predict the future, as evidenced by the issues surrounding climate change.
  • A fourth problem is that the long-term funding necessary to understand new and continuing ecological problems too often falls to a new Director or Chief who wishes to change the direction of the research or stop it altogether, so long term objectives are not supported. Another aspect of the funding problem for LTER research is the lack of substantial funding on a spatial and monetary scale that would permit comprehensive research with suitable replication. Cusser at al. (2021) have analysed LTER studies in the USA with respect to how long studies must be to achieve good results. They concluded that half of the LTER studies required 10 years or more to produce consistent results, and some required more than 20 years. Many of these LTER studies are focused on the descriptive paradigm and would not qualify as experimental ecology with specific hypotheses about community and ecosystem dynamics. LTER descriptive studies are useful for advancing knowledge of trends, but they may not be sufficient to identify and test the underlying drivers of community and ecosystem change.

Cusser, S., Helms IV, J., Bahlai, C.A., and Haddad, N.M. 2021. How long do population level field experiments need to be? Utilising data from the 40-year-old LTER network. Ecology Letters 24(5): 1103-1111. doi:10.1111/ele.13710.

Gaiser, E.E., Bell, D.M., Castorani, M.C.N., Childers, D.L., and Groffman, P.M. 2020. Long-term ecological research and evolving frameworks of disturbance ecology. Bioscience 70(2): 141-156. doi:10.1093/biosci/biz162.

Gingrich, S., Schmid, M., Dirnbock, T., Dullinger, I. et al. 2016. Long-Term Socio-Ecological Research in Practice: Lessons from Inter- and Transdisciplinary Research in the Austrian Eisenwurzen. Sustainability 8(8): 743. doi:10.3390/su8080743.

Hone, J., and Krebs, C.J. 2023. Causality and wildlife management. Journal of Wildlife Management 2023: e22412. doi:10.1002/jwmg.22412.

Krebs, C.J. 2002. Two complementary paradigms for analyzing population dynamics. Philosophical Transactions of the Royal Society of London, Series B 357: 1211-1219. doi:10.1098/rstb.2002.1122.

Lindenmayer, D.B., Burns, E.L., Tennant, P., Dickman, C.R., Green, P.T., Keith, D.A., et al. 2015. Contemplating the future: Acting now on long-term monitoring to answer 2050’s questions. Austral Ecology 40(3): 213-224. doi:10.1111/aec.12207.

Lindenmayer, D. 2018. Why is long-term ecological research and monitoring so hard to do? (And what can be done about it). Australian Zoologist 39: 576-580. doi:10.7882/az.2017.018.

Rastetter, E.B., Ohman, M.D., Elliott, K.J., Rehage, J.S., and al., e. 2021. Time lags: insights from the U.S. Long Term Ecological Research Network. Ecosphere 12(5): e03431. doi:10.1002/ecs2.3431.

Vanderbilt, K., and Gaiser, E. 2017. The International Long Term Ecological Research Network: a platform for collaboration. Ecosphere 8(2): e01697. doi:10.1002/ecs2.1697.

On the Forest Industry and why we might worry about our forests

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This blog differs from my usual in being a plea to read one book on the forest industry in Australia and why we might worry in every country about our forestry practices. The book is by David Lindenmayer and in it is a capsular summary of the evidence presented about the state of the forest industry in Australia and the myths that are continually presented about sustainability of forests wherever they are. If you teach students or adults about environmental problems, you could discuss whether these myths apply to your part of the world. For more details on the evidence behind these statements read the book. Much action is needed to eliminate these myths.

Lindenmayer, David. (2024) “The Forest Wars” Allen and Unwin, Sydney, Australia. ISBN: 978 76147 075 2.  Available as a paperback or a kindle book from Allen and Unwin.
Allen and Unwin: The Forest Wars

I copy here the opening 2 pages of this book:

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The Myths

  1. The forest will grow back.
  2. Animals just move on.
  3. Only a small proportion of the forestry estate is logged.
  4. Only small patches of the forest are logged each year, so the forest remains intact.
  5. Forestry departments do proper pre-logging surveys for animals.
  6. Detecting more animals means a species is OK.
  7. Retaining a few big trees on logged sites will save the animals.
  8. We can solve the animal housing crisis with nest boxes.
  9. Selective logging is better than clearfelling.
  10. It is good to ‘clean up’ storm and fire-damaged forest.
  11. Forest gardening heals country.
  12. We need to log forests to keep them safe.
  13. Forests will be less flammable if we thin them.
  14. Logging has the same effects as wildfires.
  15. We can burn our way out of the wildfire problem.
  16. Tall, wet forests [in Australia] were open and park-like at the time of the British invasion.
  17. Cultural burning was practiced all over Australia.
  18. Most wood cut in a logging coupe gets milled for sawn lumber.
  19. We need native forest logging to build and furnish our houses.
  20. Without native forest logging we will bring in imports that kill Orangutans.
  21. Native forest logging is value added.
  22. Logging is good business.
  23. Logging is more lucrative than other forest uses.
  24. Native forest logging employs tens of thousands of people.
  25. Australia has the best regulated native forest logging industry in the world.
  26. Government regulators will keep the bastards honest.
  27. Regulations protect old-growth forest.
  28. Regional forest agreements solved the forest wars.
  29. Melbourne is not Moscow – the spy who came in from the forest.
  30. Native forest logging is sustainable.
  31. Forest industries are enduring industries.
  32. There is nothing to worry about – carbon dioxide is natural.
  33. The best way to tackle climate change is to cut down forests and regrow them.
  34. Burning biomass from forests is better than burning fossil fuels.
  35. We already have enough reserves.
  36. Intact forests are selected for reserves.
  37. Politicians keep their promises about preserving forests.

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With thanks to David Lindemayer and his research group in Canberra.

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On Discourse and Evidence

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A major problem that bedevils society as well as science today is the distinction between opinion and evidence. The world is awash in opinions and short of evidence for many questions that fill the news media as well as the scientific literature. In trying to evaluate this statement we should recognize that there are many important issues for which we have no evidence but only beliefs. A current discussion in the news media is whether or not any particular country should spend 2% of its GDP on military expenses. Much hot air follows from these discussions because as an individual person you have no evidence one way or the other for different points of view, spend more, spend less. You will have an opinion that everyone should respect but for this and many issues any evidence that can be cited is vague. Discussion on many issues like this example are important and should be civilized but often are not.

But there are a range of issues for which scientific evidence is available. The first rule of discourse on these issues ought to be that you as a person are allowed to have any opinion you wish but you must be able to present evidence to support your opinions. You should be allowed as a person to proclaim that the Earth is not round but flat and provide the evidence one way or the other. More serious issues with different opinions involve issues like vaccination for a particular disease. On these issues scientists can only advise and provide evidence. But if a vaccine X for example has a complex side effect rate of 1 person in 1000, you could always argue that you are that one person and you are opposed to vaccination X.

How does all this relate to ecological science? First, we should recognize that many of the arguments in the ecological literature are about opinions rather than evidence. In many cases this should lead to more studies of particular problems to gather more evidence. But as we see with climate change research the evidence is accumulating but varies greatly in quality and time span from area to area and from taxonomic group to taxonomic group. We cannot agree whether our research should be focused on the oceans or on land, or on birds rather than mammals or insects. We cannot do everything, and the consequence is that ecological research funding is driven in many directions depending on who is on the committee dispersing funding and what their opinions are. The result is that for large scale problems like climate change we have convinced most people that it is a reality, but we cannot agree on the details of future change. So, we build models with past data and try to project them into the future with uncertain confidence.

The consequence is that the ecological world is awash in opinions in the same way as other parts of society, and in support of opinions the evidence often gets lost. The main problem here is that opinions are generated rapidly while evidence accumulates slowly. We see this more readily in medical science in which the media trumpets treatment X rather than Y with opinions and little evidence. We cannot demand answers to important questions tomorrow when the problem spans years or decades for evidence to accumulate. 

Ecology like every science becomes more complicated with age, and a fisheries biologist trained 40 years ago lives in a different world from one trained today. The accumulated evidence from research changes our list of important questions illustrated well by the reviews of progress in conservation science by Sutherland et al. (2022, 2023) and Christie et al. (2023), in predator- prey dynamics by Sheriff et al. (2020), wildlife management by Hone et al. (2023), and in insect conservation by Saunders et al. (2020). Understanding and solving ecology problems must rely more on evidence and less on opinions.

Christie, A.P., Christie, A.P., Morgan, W.H. & Sutherland, W.J. (2023) Assessing diverse evidence to improve conservation decision‐making. Conservation Science and Practice, 5, e13024.doi. 10.1111/csp2.13024

Hone, J., Drake, A. & Krebs, C.J. (2023) Evaluation Options for Wildlife Management and Strengthening of Causal Inference. BioScience, 73, 48-58.doi: 10.1093/biosci/biac105.

Saunders, M.E., Janes, J.K. & O’Hanlon, J.C. (2020) Moving On from the Insect Apocalypse Narrative: Engaging with Evidence-Based Insect Conservation. BioScience, 70, 80-89.doi: 10.1093/biosci/biz143.

Sheriff, M.J., Peacor, S.D., Hawlena, D. & Thaker, M. (2020) Non-consumptive predator effects on prey population size: A dearth of evidence. Journal of Animal Ecology, 89, 1302-1316.doi: 10.1111/1365-2656.13213.

Sutherland, W.J. & Jake M. Robinson, D.C.A., Tim Alamenciak, Matthew Armes, Nina Baranduin, Andrew J. Bladon, Martin F. Breed, Nicki Dyas, Chris S. Elphick, Richard A. Griffiths, Jonny Hughes, Beccy Middleton, Nick A. Littlewood, Roger Mitchell, William H. Morgan, Roy Mosley, Silviu O. Petrovan, Kit Prendergast, Euan G. Ritchie,Hugh Raven, Rebecca K. Smith, Sarah H. Watts, Ann Thornton (2022) Creating testable questions in practical conservation: a process and 100 questions. Conservation Evidence Journal, 19, 1-7.doi. 10.52201/CEJ19XIFF2753

Sutherland, W.J., Sutherland, W.J., Bennett, C. & Thornton, A. (2023) A global biological conservation horizon scan of issues for 2023. Trends in Ecology & Evolution, 38, 96-107.doi. 10.1016/j.tree.2022.10.005

Biodiversity Science

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Protecting biodiversity is a goal of most people who value the environment. My question is what are the goals of biodiversity science and how do we achieve them? Some history is in order here. The term ‘biodiversity’ was coined in the 1980s as the complete biosphere including all species and ecosystems on Earth. The idea of cataloguing all the species on Earth was present many decades before this time, since the origin of the biological sciences. By the 1990s ‘biodiversity conservation’ became a popular subject and has grown greatly since then as a companion to CO2 emissions and the climate change problem. The twin broad goals of biodiversity science and biodiversity conservation are (1) to name and describe all the species on Earth, and (2), to protect all species from extinction, preventing a loss of biodiversity. How can we achieve these two goals?

The first goal of describing species faces challenges from disagreements over what a species is or is not. The old description of a species was to describe what group it was part of, and then how different this particular species was from other members of the group. In the good old days this was primarily based on reproductive incompatibility between species, if no successful reproduction, must be a new species. This simple common-sense view was subject to many attacks since some organisms that we see as different can in fact interbreed. Lions and tigers breed together and are an example, but if their interbred offspring are sterile, clearly, they are two different species. But many arguments arose because there was no data available for 99% of species to know if they could interbreed or not. The fallback position has been to describe the anatomy of a potential species and its relatives and judge from anatomy how different they were. Endless arguments followed, egged on by naturalists who pointed out that if the elephants in India were separated by a continent from elephants in Africa, clearly, they must be different species defined by geography. Many academic wars were fought over these issues.

Then in 1953 the structure of DNA was unravelled, and a new era dawned because with advances in technology of decoding genes we could describe species in a completely new way by determining how much DNA they had in common. But what is the magic percentage of common DNA? Humans and chimpanzees have 98.6% of their DNA in common, but despite this high similarity no one argues that they are the same species.

Despite this uncertainty the answer now seems much simpler: sequence the DNA of everything and you will have the true tree of life for defining separate species. While this was a dream 20 years ago, it is now a technical reality with rapid sequencing methods to help us get criminals and define species. Problem (1) solved?

Enter the lonely ecologist into this fray. Ecologists do not just want names, they wish to understand the function of each of the ‘species’ within communities and ecosystems, how does all this biodiversity interact to produce what we see in the landscape? For the moment we have approximately 10 million species on Earth, but somewhere around 80% of these ‘species’ are still undescribed. So now we have a clash of biodiversity visions, we cannot describe all the species on Earth even on the time scale of centuries, so we cannot achieve goal (1) of biodiversity science in any reasonable time. We have measured the DNA sequence of thousands of organisms that we can capture but we cannot describe them formally as species in the older sense. Perhaps it is akin to having all the phone numbers in the New York City phone book but not knowing to whom the numbers belong.

But the more immediate problem comes with objective (2) to prevent extinctions. Enter the conservation ecologist. The first problem is discussed above, we ecologists have no way of knowing how many species are in danger of extinction. We must look for rare or declining species, but we have complete inventory for few places on Earth. We must concentrate on large mammals and birds, and hope that they act as umbrella species and represent all of biodiversity. When we do have information on threatened species, for the most part there is no money to do the ecological studies needed to reverse declines in abundance. If there is money to list species and give a recovery plan on paper, then we find there is no money to implement the recovery plan. The Species-At-Risk act in Canada was passed in 2002 and has generated many recovery plans mostly for vertebrate species that have come to their attention. Almost none of these recovery plans have been completed, so in general we are all in favour of preventing extinctions but only it if costs us nothing. By and large the politics of preventing extinctions is very strongly supported, but the economic value of extinctions is nearly zero.

None of this is very cheery to conservation biologists. Two approaches have been suggested. The first is Big Science, use satellites and drones to scan the Earth every year to describe changes in landscapes and from these images infer biodiversity ‘health’. Simple and very expensive with AI to the rescue. But while we can see largescale landscape changes, the crux is to do something about them, and it is here that we fail because of the wall of climate change that we have no control over at present. Big Science may well assist us in seeing patterns of change, but it produces no path to understanding food webs or mediating changes in threatened populations. The second is small-scale biodiversity studies that focus on what species are present, how their numbers are changing, and what are the causes of change. Difficult, possible, but very expensive because you must put biologists in the field, on the ground to do the relevant measurements over a long-time frame. The techniques are there to use, thanks to much work on ecological methods in the past. What is missing again is the money. There are a few good examples of this small-scale approach but without good organization and good funding many of these attempts stop after too few years of data.

We are left with a dilemma of a particular science, Biodiversity Science, that has no way of achieving either of its two main objectives to name and to protect species on a global level. On a local level we can adopt partial methods of success by designating and protecting national parks and marine protected areas, and by studying only a few important species, the keystone species of food webs. But then we need extensive research to determine how to protect these areas and species from the inexorable march of climate change, which has singlehandedly complicated achieving biodiversity science’s two goals. Alas at the present time we may have another science to join the description of economics as a “dismal science” And we have not even started to discuss bacteria, viruses, and fungi.

Coffey, B. & Wescott, G. (2010) New directions in biodiversity policy and governance? A critique of Victoria’s Land and Biodiversity White Paper. Australasian Journal of Environmental Management 17: 204-214. doi: 10.1080/14486563.2010.9725268.

Donfrancesco, V., Allen, B.L., Appleby, R., et al. (2023) Understanding conflict among experts working on controversial species: A case study on the Australian dingo. Conservation Science and Practice 5: e12900. doi: 10.1111/csp2.12900.

Ritchie, J., Skerrett, M. & Glasgow, A. (2023) Young people’s climate leadership in Aotearoa. Journal of Peace Education, 12-2023: 1-23. doi: 10.1080/17400201.2023.2289649.

Sengupta, A., Bhan, M., Bhatia, S., Joshi, A., Kuriakose, S. & Seshadri, K.S. (2024) Realizing “30 × 30” in India: The potential, the challenges, and the way forward. Conservation Letters 2024, e13004. doi: 10.1111/conl.13004.

Wang, Q., Li, X.C. & Zhou, X.H. (2023) New shortcut for conservation: The combination management strategy of “keystone species” plus “umbrella species” based on food web structure. Biological Conservation 286: 110265.doi. 10.1016/j.biocon.2023.110265.

Do We Need to Replicate Ecological Experiments?

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If you read papers on the philosophy of science you will very quickly come across the concept of replication, the requirement to test the same hypothesis twice or more before you become too attached to your conclusions. As a new student or a research scientist you face this problem when you wish to replicate some previous study. If you do replicate, you risk being classed as an inferior scientist with no ideas of your own. If you refuse to replicate and try something new, you will be criticized as reckless and not building a solid foundation in your science.  

There is an excellent literature discussing the problem of replication in ecology in particular and science in general. Nichols et al. (2019) argue persuasively that a single experiment is not enough. Amrheim et al. (2019) approach the problem from a statistical point of view and caution that single statistical tests are a shaky platform for drawing solid conclusions. They point out that statistical tests not only test hypotheses, but also countless assumptions and particularly for ecological studies the exact plant and animal community in which the study takes place. In contrast to ecological science, medicine probably has more replication problems at the other extreme – too many replications – leading to a waste of research money and talent. (Siontis and Ioannidis 2018).

A graduate seminar could profitably focus on a list of the most critical experiments or generalizations of our time in any subdiscipline of ecology. Given such a list we could ask if the conclusions still stand as time has passed, or perhaps if climate change has upset the older predictions, or whether the observations or experiments have been replicated to test the strength of conclusions. We can develop a stronger science of ecology only if we recognize both the strengths and the limitations of our current ideas.

Baker (2016) approached this issue by asking the simple question “Is there a reproducibility crisis?” Her results are well worth visiting. She had to cast a wide net in the sciences so unfortunately there are no details specific to ecological science in this paper. A similar question in ecology would have to distinguish observational studies and experimental manipulations to narrow down a current view of this issue. An interesting example is explored in Parker (2013) who analyzed a particular hypothesis in evolutionary biology about plumage colour in a single bird species, and the array of problems of an extensive literature on sexual selection in this field is astonishing.

A critic might argue that ecology is largely a descriptive science that should not expect to develop observational or experimental conclusions that will extend very much beyond the present. If that is the case, one might argue that replication over time is important for deciding when an established principle is no longer valid. Ecological predictions based on current knowledge may have much less reliability than we would hope, but the only way to find out is to replicate. Scientific progress depends on identifying goals and determining how far we have progressed to achieving these goals (Currie 2019). To advance we need to discuss replication in ecology.

Amrhein, V., Trafinnow, D. & Greenland, S. (2019) Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. American Statistician, 73, 262-270. doi: 10.1080/00031305.2018.1543137.

Baker, M. (2016) Is there a reproducibility crisis in science? Nature, 533, 452-454.

Currie, D.J. (2019) Where Newton might have taken ecology. Global Ecology and Biogeography, 28, 18-27. doi: 10.1111/geb.12842.

Nichols, J.D., Kendall, W.L. & Boomer, G.S. (2019) Accumulating evidence in ecology: Once is not enough. Ecology and Evolution, 9, 13991-14004. doi: 10.1002/ece3.5836.

Parker, T.H. (2013) What do we really know about the signalling role of plumage colour in blue tits? A case study of impediments to progress in evolutionary biology. Biological Reviews, 88, 511-536. doi: 10.1111/brv.12013.

Siontis, K.C. & Ioannidis, J.P.A. (2018) Replication, duplication, and waste in a quarter million systematic reviews and meta-analyses. Circulation: Cardiovascular quality and outcomes, 11, e005212. doi: 10.1161/CIRCOUTCOMES.118.005212.

On Ecology and Medicine

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As I grow older and interact more with doctors, it occurred to me that the two sciences of medicine and ecology have very much in common. That is probably not a very new idea, but it may be worth spending time on looking at the similarities and differences of these two areas of science that impinge on our lives. The key question for both is how do we sort out problems? Ecologists worry about population, community and ecosystem problems that have two distinguishing features. First, the problems are complex and the major finding of this generation of ecologists is to begin to understand and appreciate how complex they are. Second, the major problems that need solving to improve conservation and wildlife management are difficult to study with the classical tools of experimental, manipulative scientific methods. We do what we can to achieve scientific paradigms but there are many loose ends we can only wave our hands about. As an example, take any community or ecosystem under threat of global warming. If we heat up the oceans, many corals will die along with the many animals that depend on them. But not all corals will die, nor will all the fish and invertebrate species, and the ecologists is asked to predict what will happen to this ecosystem under global warming. We may well understand from rigorous laboratory research about temperature tolerances of corals, but to apply this to the real world of corals in oceans undergoing many chemical and physical changes we can only make some approximate guesses. We can argue adaptation, but we do not know the limits or the many possible directions of what we predict will happen.

Now consider the poor physician who must deal with only one species, Homo sapiens, and the many interacting organs in the body, the large number of possible diseases that can affect our well-being, the stresses and strains that we ourselves cause, and the physician must make a judgement of what to do to solve your particular problem. If you have a broken arm, it is simple thankfully. If you have severe headaches or dizziness, many different causes come into play. There is no need to go into details that we all appreciate, but the key point is that physicians must solve problems of health with judgements but typically with no ability to do the kinds of experimental work we can do with mice or rabbits in the laboratory. And the result is that the physician’s judgements may be wrong in some cases, leading possibly to lawyers arguing for damages, and one appreciates that once we leave the world of medical science and enter the world of lawyers, all hope for solutions is near impossible.

There is now some hope that artificial intelligence will solve many of these problems both in ecological science and in medicine, but this belief is based on the premise that we know everything, and the only problem is to find the solutions in some forgotten textbook or scientific paper that has escaped our memory as humans. To ask that artificial intelligence will solve these basic problems is problematic because AI depends on past knowledge and science solves problems by future research.

Everyone is in favour of personal good health, but alas not everyone favours good environmental science because money is involved. We live in a world where major problems with climate change have had solutions presented for more than 50 years, but little more than words are presented as the solutions rather than action. This highlights one of the main differences between medicine and ecology. Medical issues are immediate since we have active lives and a limited time span of life. Ecological issues are long-term and rarely present an immediate short-term solution. Setting aside protected areas is in the best cases a long-term solution to conservation issues, but money for field research is never long term and ecologists do not live forever. Success stories for endangered species often require 10-20 years or more before success can be achieved; research grants are typically presented as 3- or 5-year proposals. The time scale we face as ecologists is like that of climate scientists. In a world of immediate daily concerns in medicine as in ecology, long-term problems are easily lost to view.

There has been an explosion of papers in the last few years on artificial intelligence as a potentially key process to use for answering both ecological and medical questions (e.g. Buchelt et al. 2024, Christin, Hervet, and Lecomte, 2019, Desjardins-Proulx, Poisot, & Gravel, 2019). It remains to be seen exactly how AI will help us to answer complex questions in ecology and medicine. AI is very good in looking back, but will it be useful to solve our current and future problems? Perhaps we still need to continue training good experimental scientists in ecology and in medicine.  

Buchelt, A., Buchelt, A., Adrowitzer, A. & Holzinger, A. (2024) Exploring artificial intelligence for applications of drones in forest ecology and management. Forest Ecology and Management, 551, 121530. doi: 10.1016/j.foreco.2023.121530.

Christin, S., Hervet, É. & Lecomte, N. (2019) Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632-1644. doi: 10.1111/2041-210X.13256.

Desjardins-Proulx, P., Poisot, T. & Gravel, D. (2019) Artificial Intelligence for ecological and evolutionary synthesis. Frontiers in Ecology and Evolution, 7. doi: 10.3389/fevo.2019.00402.

The Problem of Time in Ecology

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There is a problem in doing ecological studies that is too little discussed – what is the time frame of a good study? The normal response would be that the time frame varies with each study so that no guidelines can be provided. There is increasing recognition that more long-term studies are needed in ecology (e.g. Hughes et al. 2017) but the guidelines remain unclear.

The first issue is usually to specify a time frame, e.g. 5 years, 10 years. But this puts the cart before the horse, as the first step ought to be to define the hypothesis being investigated. In practice hypotheses in many ecological papers are poorly presented and there should not be one hypothesis but a series of alternative hypotheses. Given that, the question of time can be given with more insight. How many replicated time periods do you need to measure the ecological variables in the study? If your time scale unit is one year, 2 or 3 years is not enough to come to any except very tentative conclusions. We have instantly fallen into a central dilemma of ecology – studies are typically planned and financed on a 3–5-year time scale, the scale of university degrees.

Now we come up against the fact of climate change and the dilemma of trying to understand a changing system when almost all field work assumes an unchanging environment. Taken to some extreme we might argue that what happens in this decade tells us little about what will happen in the next decade. The way around this problem is to design experiments to test the variables that are changing ahead of time, e.g., what a 5⁰C temperature increase will do to the survival of your corals. To follow this approach, which is the classic experimental approach of science, we must assume we know the major variables affecting our population or community changes. At present we do not know the answer to this question, and we rely on correlations of a few variables as predictors of how large a change to expect.

There is no way out of this empirical box, which defines clearly how physics and chemistry differ from ecology and medicine. There are already many large-scale illustrations of this problem. Forest companies cut down old-growth timber on the assumption that they can get the forest back by replanting seedlings in the harvested area. But what species of tree seedlings should we replant if we are concerned that reforestation often operates on a 100–500-year time scale? And in most cases, there is no consideration of the total disruption of the ecosystem, and we ignore all the non-harvestable biodiversity. Much research is now available on reforestation and the ecological problems it produces. Hole-nesting birds can be threatened if old trees with holes are removed for forestry or agricultural clearing (Saunders et al. 2023). Replanting trees after fire in British Columbia did not increase carbon storage over 55 years of recovery when compared with unplanted sites (Clason et al. 2022). Consequently, in some forest ecosystems tree planting may not be useful if carbon storage is the desired goal.

At the least we should have more long-term monitoring of the survival of replanted forest tree seedlings so that the economics of planting could be evaluated. Short-term Australian studies in replanted agricultural fields showed over 4 years differences in survival of different plant species (Jellinek et al. 2020). For an on-the-ground point of view story about tree planting in British Columbia see:
https://thetyee.ca/Opinion/2023/11/02/Dont-Thank-Me-Being-Tree-Planter/. But we need longer-term studies on control and replanted sites to be more certain of effective restoration management. Gibson et al. (2022) highlighted the fact that citizen science over a 20-year study could make a major contribution to measuring the effectiveness of replanting. Money is always in short supply in field ecology and citizen science is one way of achieving goals without too much cost. 

Forest restoration is only one example of applied ecology in which long-term studies are too infrequent. The scale of restoration of temperate and boreal ecosystems is around 100 years, and this points to one of the main failures of long-term studies, that they are difficult to carry on after the retirement of the principal investigators who designed the studies.

The Park Grass Experiment begun in 1856 on 2.8 ha of grassland in England is the oldest ecological experiment in existence (Silvertown et al. 2006). As such it is worth a careful evaluation for the questions it asked and did not ask, for the scale of the experiment, and for the experimental design. It raises the question of generality for all long-term studies and cautions us about the utility and viability of many of the large-scale, long-term studies now in progress or planned for the future.

The message of this discussion is that we should plan for long-term studies for most of our critical ecological problems with clear hypotheses of how to conserve biodiversity and manage our agricultural landscapes and forests. We should move away from 2–3-year thesis projects on isolated issues and concentrate on team efforts that address critical long-term issues with specific hypotheses. Which says in a nutshell that we must develop a vision that goes beyond our past practices in scatter-shot, short-term ecology and at the same time avoid poorly designed long-term studies of the future.

Clason, A.J., Farnell, I. & Lilles, E.B. (2022) Carbon 5–60 Years After Fire: Planting Trees Does Not Compensate for Losses in Dead Wood Stores. Frontiers in Forests and Global Change, 5, 868024. doi: 10.3389/ffgc.2022.868024.

Gibson, M., Maron, M., Taws, N., Simmonds, J.S. & Walsh, J.C. (2022) Use of citizen science datasets to test effects of grazing exclusion and replanting on Australian woodland birds. Restoration Ecology, 30, e13610. doi: 10.1111/rec.13610.

Hughes, B.B.,et al. (2017) Long-term studies contribute disproportionately to ecology and policy. BioScience, 67, 271-281. doi.: 10.1093/biosci/biw185.

Jellinek, S., Harrison, P.A., Tuck, J. & Te, T. (2020) Replanting agricultural landscapes: how well do plants survive after habitat restoration? Restoration Ecology, 28, 1454-1463. doi: 10.1111/rec.13242.

Saunders, D.A., Dawson, R. & Mawson, P.R. (2023) Artificial nesting hollows for the conservation of Carnaby’s cockatoo Calyptorhynchus latirostris: definitely not a case of erect and forget. Pacific Conservation Biology, 29, 119-129. doi: 10.1071/PC21061.

Silvertown, J., Silvertown, J., Poulton, P. & Biss, P.M. (2006) The Park Grass Experiment 1856–2006: its contribution to ecology. Journal of Ecology, 94, 801-814. doi: 10.1111/j.1365-2745.2006.01145.x.

The Ecological Outlook

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There is an extensive literature on ecological traps going back two decades (e.g. Schlaepfer et al. 2002, Kristan 2003, Battin 2004) discussing the consequences of particular species selecting a habitat for breeding that is now unsuitable. A good example is discussed in Lamb et al. (2017) for grizzly bears in southeastern British Columbia in areas of high human contact. The ecological trap hypothesis has for the most part been discussed in relation to species threatened by human developments with some examples of whole ecosystems and human disturbances (e.g. Lindenmayer and Taylor 2020). The concept of an ecological trap can be applied to the Whole Earth Ecosystem, as has been detailed in the IPCC 2022 reports and it is this global ecological trap that I wish to discuss.

The key question for ecologists concerned about global biodiversity is how much biodiversity will be left after the next century of human disturbances. The ecological outlook is grim as you can hear every day on the media. The global community of ecologists can ameliorate biodiversity loss but cannot stop it except on a very local scale. The ecological problem operates on a century time scale, just the same as the political and social change required to escape the global ecological trap. E.O. Wilson (2016) wrote passionately about our need to set aside half of the Earth for biodiversity. Alas, this was not to be. Dinerstein et al. (2019) reduced the target to 30% in the “30 by 30” initiative, subsequently endorsed by 100 countries by 2022. Although a noble political target, there is no scientific evidence that 30 by 30 will protect the world’s biodiversity. Saunders et al. (2023) determined that for North America only a small percentage of refugia (5– 14% in Mexico, 4–10% in Canada, and 2–6% in the USA) are currently protected under four possible warming scenarios ranging from +1.5⁰C to +4⁰C. And beyond +2⁰C refugia will be valuable only if they are at high latitudes and high elevations.

The problem as many people have stated is that we are marching into an ecological trap of the greatest dimension. A combination of global climate change and continually increasing human populations and impacts are the main driving factors, neither of which are under the control of the ecological community. What ecologists and conservationists can do is work on the social-political front to protect more areas and keep analysing the dynamics of declining species in local areas. We confront major political and social obstacles in conservation ecology, but we can increase our efforts to describe how organisms interact in natural ecosystems and how we can reduce undesirable declines in populations. All this requires much more monitoring of how ecosystems are changing on a local level and depends on how successful we can be as scientists to diagnose and solve the ecological components of ecosystem collapse.

As with all serious problems we advance by looking clearly into what we can do in the future based on what we have learned in the past. And we must recognize that these problems are multi-generational and will not be solved in any one person’s lifetime. So, as we continue to march into the ultimate ecological trap, we must rally to recognize the trap and use strong policies to reverse its adverse effects on biodiversity and ultimately to humans themselves. None of us can opt out of this challenge.

There is much need in this dilemma for good science, for good ecology, and for good education on what will reverse the continuing degradation of our planet Earth. Every bit counts. Every Greta Thunberg counts.

Battin, J. (2004) When good animals love bad habitats: ecological traps and the conservation of animal populations. Conservation Biology, 18, 1482-1491.

Dinerstein, E., Vynne, C., Sala, E., et al. (2019) A Global Deal For Nature: Guiding principles, milestones, and targets. Science Advances, 5, eaaw2869.doi: 10.1126/sciadv.aaw2869..

IPCC, 2022b. In: Skea, J., Shukla, P.R., et al. (Eds.), Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge University Press. doi: www.ipcc.ch/report/ar6/wg3/.

Kristan III, W.B. (2003) The role of habitat selection behavior in population dynamics: source–sink systems and ecological traps. Oikos, 103, 457-468.

Lamb, C.T., Mowat, G., McLellan, B.N., Nielsen, S.E. & Boutin, S. (2017) Forbidden fruit: human settlement and abundant fruit create an ecological trap for an apex omnivore. Journal of Animal Ecology, 86, 55-65. doi. 10.1111/1365-2656.12589.

Lindenmayer, D.B. and Taylor, C. (2020) New spatial analyses of Australian wildfires highlight the need for new fire, resource, and conservation policies. Proceedings of the National Academy of Sciences 117, 12481-124485. doi. 10.1073/pnas.2002269117.

Saunders, S.P., Grand, J., Bateman, B.L., Meek, M., Wilsey, C.B., Forstenhaeusler, N., Graham, E., Warren, R. & Price, J. (2023) Integrating climate-change refugia into 30 by 30 conservation planning in North America. Frontiers in Ecology and the Environment, 21, 77-84. doi. 10.1002/fee.2592.

Schlaepfer, M.A., Runge, M.C. & Sherman, P.W. (2002) Ecological and evolutionary traps. Trends in Ecology & Evolution, 17, 474-480.

Wilson, E.O. (2016) Half-Earth: Our Planet’s Fight for Life. Liveright, New York. ISBN: 978-1-63149-252-5.

The Meaningless of Random Sampling

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Statisticians tell us that random sampling is necessary for making general inferences from the particular to the general. If field ecologists accept this dictum, we can only conclude that it is very difficult to nearly impossible to reach generality. We can reach conclusions about specific local areas, and that is valuable, but much of our current ecological wisdom on populations and communities relies on the faulty model of non-random sampling. We rarely try to define the statistical ‘population’ which we are studying and attempting to make inferences about with our data. Some examples might be useful to illustrate this problem.

Marine ecologists ae mostly agreed that sea surface temperature rise is destroying coral reef ecosystems. This is certainly true, but it camouflages the fact that very few square kilometres of coral reefs like the Great Barrier Reef have been comprehensively studied with a proper sampling design (e.g. Green 1979, Lewis 2004). When we analyse the details of coral reef declines, we find that many species are affected by rising sea temperatures, but some are not, and it is possible that some species will adapt by natural selection to the higher temperatures. So we quite rightly raise the alarm about the future of coral reefs. But in doing so we neglect in many cases to specify the statistical ‘population’ to which our conclusions apply.

Most people would agree that such an approach to generalizing ecological findings is tantamount to saying the problem is “how many angels can dance on the head of a pin”, and in practice we can ignore the problem and generalize from the studied reefs to all reefs. And scientists would point out that physics and chemistry seek generality and ignore this problem because one can do chemistry in Zurich or in Toronto and use the same laws that do not change with time or place. But the ecosystems of today are not going to be the ecosystems of tomorrow, so generality in time cannot be guaranteed, as paleoecologists have long ago pointed out.

It is the spatial problem of field studies that collides most strongly with the statistical rule to random sample. Consider a hypothetical example of a large national park that has recently been burned by this year’s fires in the Northern Hemisphere. If we wish to measure the recovery process of the vegetation, we need to set out plots to resample. We have two choices: (1) lay out as many plots as possible, and sample these for several years to plot recovery. Or (2) lay out plots at random each year, never repeating the same exact areas to satisfy the specifications of statisticians to “random sample” the recovery in the park. We typically would do (1) for two reasons. Setting up new plots each year as per (2) would greatly increase the initial field work of defining the random plots and would probably mean that travel time between the plots would be greatly increased. Using approach (1) we would probably set out plots with relatively easy access from roads or trails to minimize costs of sampling. We ignore the advice of statisticians because of our real-world constraints of time and money. And we hope to answer the initial questions about recovery with this simpler design.

I could find few papers in the ecological literature that discuss this general problem of inference from the particular to the general (Ives 2018, Hauss 2018) and only one that deals with a real-world situation (Ducatez 2019). I would be glad to be sent more references on this problem by readers.

The bottom line is that if your supervisor or research coordinator criticizes your field work because your study areas are not randomly placed or your replicate sites were not chosen at random, tell him or her politely that virtually no ecological research in the field is done by truly random sampling. Does this make our research less useful for achieving ecological understanding – probably not. And we might note that medical science works in exactly the same way field ecologists work, do what you can with the money and time you have. The law that scientific knowledge requires random sampling is often a pseudo-problem in my opinion.  

Ducatez, S. (2019) Which sharks attract research? Analyses of the distribution of research effort in sharks reveal significant non-random knowledge biases. Reviews in Fish Biology and Fisheries, 29, 355-367. doi. 10.1007/s11160-019-09556-0

Green, R.H. (1979) Sampling Design and Statistical Methods for Environmental Biologists. Wiley, New York. 257 pp.

Hauss, K. (2018) Statistical Inference from Non-Random Samples. Problems in Application and Possible Solutions in Evaluation Research. Zeitschrift fur Evaluation, 17, 219-240. doi.

Ives, A.R. (2018) Informative Irreproducibility and the Use of Experiments in Ecology. BioScience, 68, 746-747. doi. 10.1093/biosci/biy090

Lewis, J. (2004) Has random sampling been neglected in coral reef faunal surveys? Coral Reefs, 23, 192-194. doi: 10.1007/s00338-004-0377-y.

The Time Frame of Ecological Science

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Ecological research differs from many branches of science in having a more convoluted time frame. Most of the sciences proceed along paths that are more often than not linear – results A → results B → results C and so on. Of course, these are never straight linear sequences and were described eloquently by Platt (1964) as strong inference:

“Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly: 1) Devising alternative hypotheses; 2) Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses; 3) Carrying out the experiment so as to get a clean result; “Recycling the procedure, making sequential hypotheses to refine the possibilities that remain; and so on. It is like climbing a tree.” (page 347 in Platt).

If there is one paper that I would recommend all ecologists read it is this paper which is old but really is timeless and critical in our scientific research. It should be a required discussion topic for every graduate student in ecology.

Some ecological science progresses as Platt (1964) suggests and makes good progress, but much of ecology is lost in a failure to specify alternative hypotheses, in changing questions, in abandoning topics because they are too difficult, and in a shortage of time. It is the time component of ecological research that I wish to discuss in this blog.

The idea of long-term studies has always been present in ecology but was perhaps brought to our focus by the compilation by Gene Likens in 1989 in a book of 14 chapters that are as vital now as they were 34 years ago. Many discussions of long-term studies are now available to examine this issue. Buma et al. (2019) for example discuss plant primary succession at Glacier Bay, Alaska which has 100 years of data, and which illustrates in a very slow ecosystem a test of conventional rules of community development. Cusser et al. (2021) follow this by asking a critical question of how long field experiments need to be. They restrict long-term to be > 10 years of study and used data from the USA LTER sites. This question depends very much on the community or ecosystem of study. Studies in areas with a stable climate produced results more quickly than those in highly seasonal environments, and plant studies needed to be longer term than animal studies to reach stable conclusions. Ten years may not be enough.

Reinke et al. (2019) reviewed 3 long term field studies and suggest that long-term studies can be useful to allow us to predict how ecosystems will change with time. All these studies lead to three unanswered questions that are critical for progress in ecology. The first question is how we decide as a community exactly which ecological system we should be studying long-term. No one knows how to answer this question, and a useful graduate seminar could debate the utility of what are now considered model long-term studies, such as the three highlighted in Reinke et al. (2019) or the Park Grass Experiment (Addy et al. 2022). At the moment these decisions are opportunistic, and we should debate how best to proceed. Clearly, we cannot do everything for every population and community of interest, so how do we choose? We need model systems that can be applied to a wide variety of environments across the globe and that ask questions of global significance. Many groups of ecologists are trying to do this, but a host of decisions about who to fund and support in what institution are vital to avoid long-term studies driven more by convenience than by ecological importance.

A second question involves the implied disagreement whether many important questions in ecology today could be answered by short-term studies, so we reach a position where there is competition between short- and long-term funding. These decisions about where to do what for how long are largely uncontrolled. One would prefer to see an articulated set of hypotheses and predictions to proceed with decision making, whether for short-term studies suitable for graduate students or particularly for long-term studies that exceed the life of individual researchers.

A third question is the most difficult one of the objectives of long-term research. Given climate change as it is moving today, the hope that long-term studies will give us reliable predictions of changes in communities and ecosystems is at risk, the same problem of extrapolating a regression line beyond the range of the data. Depending on the answer to this climate dilemma, we could drop back to the suggestion that because we have only a poor ability to predict ecological change, we should concentrate more on widespread monitoring programs and less on highly localized studies of a few sites that are of unknown generality. Testing models with long-term data is enriching the ecological literature (e.g. Addy et al 2022). But the challenge is whether our current understanding is sufficient to make predictions for future populations or communities. Should ecology adopt the paradigm of global weather stations?

Addy, J.W.G., Ellis, R.H., MacLaren, C., Macdonald, A.J., Semenov, M.A. & Mead, A. (2022) A heteroskedastic model of Park Grass spring hay yields in response to weather suggests continuing yield decline with climate change in future decades. Journal of the Royal Society Interface, 19, 20220361. doi: 10.1098/rsif.2022.0361.

Buma, B., Bisbing, S.M., Wiles, G. & Bidlack, A.L. (2019) 100 yr of primary succession highlights stochasticity and competition driving community establishment and stability. Ecology, 100, e02885. doi: 10.1002/ecy.2885.

Cusser, S., Helms IV, J., Bahlai, C.A. & Haddad, N.M. (2021) How long do population level field experiments need to be? Utilising data from the 40-year-old LTER network. Ecology Letters, 24, 1103-1111. doi: 10.1111/ele.13710.

Hughes, B.B., Beas-Luna, R., Barner, A., et al. (2017) Long-term studies contribute disproportionately to ecology and policy. BioScience, 67, 271-281. doi: 10.1093/biosci/biw185.

Likens, G.E. (Editor, 1989) Long-term Studies in Ecology: Approaches and Alternatives. Springer Verlag, New York. 214 pp. ISBN: 0387967435.

Platt, J.R. (1964) Strong inference. Science, 146, 347-353. doi: 10.1126/science.146.3642.347.

Reinke, B.A., Miller, D.A.W. & Janzen, F.J. (2019) What have long-term field studies taught as about population dynamics? Annual Review of Ecology, Evolution, and Systematics, 50, 261-278. doi: 10.1146/annurev-ecolsys-110218-024717.